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Double-Track PRML Detection for Two-Track Reading with a Wide-Track Reader in Shingled Magnetic Recording Systems
(2025-09-01)
Khametong, Anawin
;
Greaves, Simon John
;
Warisarn, Chanon
The utilization of two-track simultaneous reading is proposed to avoid the requirement for a narrow track reader in shingled magnetic recording systems, where partial response maximum likelihood detection and recursive decoding by oversampling techniques are employed for decoding. To develop effective decoding techniques when reading two tracks with a wide-track reader, we propose utilizing a pre-coding scheme along with a modified Viterbi detector. A pre-coder and an oversampling scheme are first adopted, where the sampling points are located at the centers of the front and rear halves of the recorded bits. The Trellis diagram of the conventional Viterbi algorithm is then modified according to all possible transitions of the readback signal obtained from two-track simultaneous reading. The proposed technique can simultaneously detect two data tracks. Simulation results indicate that at an areal density of 2 Tb/in2, the proposed system offers improved performance regarding the bit-error rate.
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Study of CNN-Based Data Detection in Dual-Layer Bit-Patterned Magnetic Recording Systems
(2025-01-01)
Sangthong, Siraphop
;
Sokjabok, Siwakon
;
Khametong, Anawin
;
Warisarn, Chanon
This paper introduces an innovative data detection system that utilizes convolutional neural networks (CNNs) for dual-layered bit-patterned magnetic systems. Using a mutual-information CNN architecture, the proposed system tackles the challenge of decoding overlapping readback signals from upper and lower layers. The sliding window detection schemes are implemented with input lengths of 6 (2×3) and 14 (2×7) bits, processing oversampled readback signals from a dataset of 1,000,000 bits. Simulation results conducted over a signal-to-noise ratio range of 10 to 24 dBs indicate that the CNN model with a larger input window significantly outperforms smaller input models and conventional partial response maximum likelihood detectors in terms of bit error rate. These findings illustrate the effectiveness of CNN-based detection in enhancing classification accuracy under high-noise conditions, paving the way for future ultra-high-density magnetic recording systems.
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Mutual Soft-Information Improvement Techniques for Lower Layer Performance Improvement in Double-Layered Magnetic Recording Systems
(2025-01-01)
Khametong, Anawin
;
Greaves, Simon John
;
Warisarn, Chanon
Double-layer magnetic recording (DLMR) development aims to increase areal density (AD) by stacking multiple recording layers on the same platter. This has the potential to revolutionize magnetic recording. On the other hand, bit-patterned magnetic recording (BPMR) uses nanoscale bit islands to enhance storage density, reduce transition noise, and maintain thermal stability. This study focuses on combining BPMR and DLMR to further increase AD. Unfortunately, this approach encounters difficulties in separating data from different layers within a single readback signal, which results in poor performance. Retrieving data from the lower layer is additionally complicated by its distance from the reader, leading to degraded bit-error-rate (BER) performance. To address this, we have developed mutual soft-information improvement techniques (MIITs) such as the maximum soft-information (MSI) and sum soft-information (SSI) schemes. These techniques enhance the soft-information output obtained from a soft-output Viterbi algorithm (SOVA) detector. Additionally, we propose three iterative systems that exchange soft information between SOVA and low-density parity-check (LDPC) codes. Our demonstrations show improved BER performance for both upper and lower layers, compared to conventional systems with single-layer media at the same AD of 4 Tb/in2
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A Study of Iterative Signal Processing Schemes for Double-Layered Bit-Patterned Recording
(2024-01-01)
Khametong, Anawin
;
Warisarn, Chanon
To enhance the areal recording density of an alternated magnetic recording technology, we have proposed iterative signal processing schemes based on a double-layered bit-patterned magnetic recording (BPMR) system. A read/write channel model that ideally integrates the signals from both upper and lower recording media layers was created. A single reader was adopted to retrieve a single readback signal through a combination of upper and lower layer readback signals. We first initially recover a signal of the upper layer utilizing the partial response maximum likelihood (PRML) technique that provides a pair of generalized partial response targets. Then, the estimated recorded data sequence obtained from iterative processing is used to reproduce the individual readback signals, and both of them will then be detected with their proper target and equalizer that were designed with each reproduced readback signal. Based on the results, the proposed iterative signal processing schemes have been certified to deliver better bit error rate (BER) performance compared to single-layer BPMR systems, taking into account the total areal density (AD) at 4.0 Tb/in2.
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A Study of Iterative Signal Processing Schemes for Double-Layered Bit-Patterned Recording
(2024-01-01)
Khametong, Anawin
;
Warisarn, Chanon
To enhance the areal recording density of the alternated magnetic recording technologies, we have investigated the performances of the iterative recording schemes based on a double-layered bit-patterned magnetic recording (BPMR). We developed a read/write channel model that ideally integrates the signals from both upper and lower layers. In this study, we considered a two-layer recording that uses a single reader to retrieve a single readback signal through a signal combination produced from the upper and lower layers. For signal processing, we initially recover the upper layer signal and subsequently recover the lower layer signal using partial equalization and readback reconstruction schemes where the reproduced readback signals were generated under the iterative system. Based on the results, the proposed iterative signal processing schemes have been certified to deliver better bit error rate performance compared to single-layer BPMR systems, when they were considered at the total areal density of 4 Tb/in2
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A Linear Support Vector Machine Based Detector for Bit-Patterned Magnetic Recording
(2023-01-01)
Khametong, Anawin
;
Koonkarnkhai, Santi
;
Kovintavewat, Piya
;
Warisarn, Chanon
The demand for high-capacity storage devices for storing digital information is continuously increasing because of the rapid growth in the number of social media users. Alternative magnetic recording technologies, such as bit-patterned magnetic recording (BPMR), have been proposed in parallel with the current perpendicular magnetic recording technology. However, to increase the areal density in BPMR, we unavoidably encounter the problems of two-dimensional (2D) interference and track mis-registration (TMR). Consequently, to solve these problems, we first present the modified soft-information adjuster (SIA) to mitigate the 2D interference and improve the log-likelihood ratios (LLRs) that were initially produced from the conventional detectors. Then, we propose a linear support vector machine (LSVM)-based detector that works with the modified SIA so as to enhance the reliability of LLRs, which can in turn provide better estimated user bits. Simulation results reveal that the proposed system can yield better bit-error rate performance and is more robust to the TMR effect than the conventional system without the LSVM-based detector.
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Deep Neural Networks based Soft-Information Improvement for Two-head/Two-Track Bit-Patterned Magnetic Recording
(2022-01-01)
Khametong, Anawin
;
Rueangnetr, Natthakan
;
Warisarn, Chanon
;
Koonkarnkhai, Santi
;
Kovintavewat, Piya
To increase an areal density (AD) of an ultra-high density bit-patterned magnetic recording (BPMR) system, we have previously proposed a track misregistration (TMR) correction method combined with the soft information adjustor (SIA) to cope with the effects of TMR and two-dimensional (2D) interference. However, we found that soft information or log-likelihood ratio (LLR) can be improved to earn better bit-error-rate (BER) performances. In this work; therefore, we propose to use two types of deep neural networks (DNNs), i.e., multi-layer perceptron (MLP) and long short-Term memory (LSTM) network with identical parameter magnitude to improve overall system performance. Here, both DNNs are operated with an earlier SIA on a two-head/two-Track (2H2T) BPMR system. Numerical results show that our proposed methods can deliver better BER performance over the earlier SIA system at all TMR levels with and without position jitter noises at the AD of 3.0 Terabit per square inch.

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